1 Detailed explanation

For a detailed manual for this section please access this link:

1. Data menu manual

2 Sub-menu: Molecular data

In this sub-menu the user will be able to search, download and prepare The NCI’s Genomic Data Commons (GDC) data for analysis. The first step is to search for GDC open data access with the Advanced search box. The search results will be summarized in plots in the center of the screen. The second step is to download and prepare the data into a R object to be used in the analysis steps.

GDC search menu: Main window.

2.1 Useful information

Different sources: Legacy vs Harmonized

There are two available sources to download GDC data using TCGAbiolinks: - GDC Legacy Archive : provides access to an unmodified copy of data that was previously stored in CGHub and in the TCGA Data Portal hosted by the TCGA Data Coordinating Center (DCC), in which uses as references GRCh37 (hg19) and GRCh36 (hg18). - GDC harmonized database: data available was harmonized against GRCh38 (hg38) using GDC Bioinformatics Pipelines which provides methods to the standardization of biospecimen and clinical data.

Understanding the barcode

A TCGA barcode is composed of a collection of identifiers. Each specifically identifies a TCGA data element. Refer to the following figure for an illustration of how metadata identifiers comprise a barcode. An aliquot barcode contains the highest number of identifiers.

Example:

• Aliquot barcode: TCGA-G4-6317-02A-11D-2064-05
• Participant: TCGA-G4-6317
• Sample: TCGA-G4-6317-02

For more information check TCGA wiki

2.2 Advanced search box

The following filters are available to select the data:

• Database
• Project filter
• Data category filter
• Data type filter
• Workflow filter
• Sample type filter
• Platform filter
• List of barcodes
• Clinical filter: gender, race, vital status, tumor stage.

Repeated cases (barcodes)

In the search step, it is important that there are no repeated cases, otherwise the prepare step might no work. If it happens, the search will give you this warning “There are more than one file for the same case.” To solve this problem, you probably need to use more filters to correctly select your data.

As an example of this problem, if you use only Project (i.e “TCGA-ACC”) and Data category filter (i.e “Copy Number Variantion”), for each sample we have two data types “Copy Number segment”" and “Masked Copy Number segment”.

2.3 Download & Prepare box

After the search step, the user can download the data and prepare it into an R object. The data can be prepared into one of two formats:

• SummarizedExperiment (Huber et al. 2015): An R object that contains the sample information matrix (clinical and subtype information), the assay matrix (i.e DNA methylation beta-values), the features matrix (i.e information for each probe in the DNA methylation assay mamtrix).
• Data Frame: A simple table with only the assay matrix (i.e Gene expression quantification)

In case a SummarizedExperiment object was selected the user will be able to add more metadata infromation into the sample information matrix. By checking “Add gistic2 and mutation information” and giving a list of genes, the GUI will download GISTIC2 data from the GDAC firehose and Mutation Annotation Files (MAF) and their information to the object. This might be useful, for example, if the user wants to evaluate the survival of mutant samples vs non-mutant or if he wants to evaluate it on a heatmap, which can be used as labels.

Finnaly, the object will be saved with the same name as the file name field.

3 Sub-menu: Subtype data

The Cancer Genome Atlas (TCGA) Research Network has reported integrated genome-wide studies of various diseases. We have added some of the subtypes defined by these reports in our package.

• BRCA(C. G. A. Network and others 2012c)
• COAD (C. G. A. Network and others 2012a)
• GBM (Ceccarelli et al. 2016)
• HNSC (C. G. A. Network and others 2015a)
• KICH (Davis et al. 2014)
• KIRC(C. G. A. R. Network and others 2013a)
• KIRP (Network and others 2016)
• LGG(Ceccarelli et al. 2016)
• LUAD (C. G. A. R. Network and others 2014a)
• LUSC(C. G. A. R. Network and others 2012)
• PRAD(C. G. A. R. Network and others 2015)
• READ (C. G. A. Network and others 2012b)
• SKCM (C. G. A. Network and others 2015b)
• STAD (C. G. A. R. Network and others 2014a)
• THCA (C. G. A. R. Network and others 2014b)
• UCEC (C. G. A. R. Network and others 2013b)

The user can visualize the table with the information and save it as csv or R object (rda) file.

4 Sub-menu: Clinical data

TCGAbiolinks has provided a few functions to search, download and parse GDC clinical data.

4.1 Useful information

Different sources

In GDC database the clinical data can be retrieved from two sources:

• indexed clinical: a refined clinical data that is created using the XML files.
• XML files

There are two main differences:

• XML has more information: radiation, drugs information, follow-ups, biospecimen, etc. So the indexed one is only a subset of the XML files
• The indexed data contains the updated data with the follow up informaiton. For example: if the patient is alive in the first time clinical data was collect and the in the next follow-up he is dead, the indexed data will show dead. The XML will have two fields, one for the first time saying he is alive (in the clinical part) and the follow-up saying he is dead. You can see this case here:

4.2 Clinical data inconsistencies

Clinical data inconsistencies

Some inconsisentecies have been found in the indexed clinical data and are being investigated by the GDC team. These inconsistencies are:

• Vital status field is not correctly updated
• Tumor Grade field is not being filled
• Progression or Recurrence field is not being filled

4.2.1 Vital status inconsistancie

# Get XML files and parse them
clin.query <- GDCquery(project = "TCGA-READ", data.category = "Clinical", barcode = "TCGA-F5-6702")
GDCdownload(clin.query)
clinical.patient <- GDCprepare_clinic(clin.query, clinical.info = "patient")
clinical.patient.followup <- GDCprepare_clinic(clin.query, clinical.info = "follow_up")

# Get indexed data
clinical.index <- GDCquery_clinic("TCGA-READ")

select(clinical.patient,vital_status,days_to_death,days_to_last_followup) %>% datatable

select(clinical.patient.followup, vital_status,days_to_death,days_to_last_followup) %>% datatable

# Vital status should be the same in the follow up table 
filter(clinical.index,submitter_id == "TCGA-F5-6702") %>% select(vital_status,days_to_death,days_to_last_follow_up) %>% datatable

4.2.2 Progression or Recurrence and Grande inconsistancie

# Get XML files and parse them
recurrent.samples <- GDCquery(project = "TCGA-LIHC",
                             data.category = "Transcriptome Profiling",
                             data.type = "Gene Expression Quantification", 
                             workflow.type = "HTSeq - Counts",
                             sample.type =  "Recurrent Solid Tumor")$results[[1]] %>% select(cases)
recurrent.patients <- substr(recurrent.samples$cases,1,12)
clin.query <- GDCquery(project = "TCGA-LIHC", data.category = "Clinical", barcode = recurrent.patients)
GDCdownload(clin.query)
clinical.patient <- GDCprepare_clinic(clin.query, clinical.info = "patient") 

# Get indexed data
GDCquery_clinic("TCGA-LIHC") %>% filter(submitter_id %in% recurrent.patients) %>% 
    select(progression_or_recurrence,days_to_recurrence,tumor_grade) %>% datatable

# XML data
clinical.patient %>% select(bcr_patient_barcode,neoplasm_histologic_grade) %>% datatable

4.3 Tutorial Video

Tutorial Video: Fetching GDC clinical data - (http://www.youtube.com/watch?v=54NBug9ycwM)

5 Sub-menu: Mutation data

In order to download the Mutation Annotation Format (MAF), we provide the user with an interface to the GDCquery_maf function. Briefly, it will download the open acess maf files from https://gdc-docs.nci.nih.gov/Data/Release_Notes/Data_Release_Notes/. Four separate variant calling pipelines are implemented for GDC data harmonization which are described here.

5.1 Mutatation data search box

The available filters are:

• Tumor filter: a TCGA tumor, such as Breast invasive Carcinoma (TCGA-BRCA)
• Variant calling pipelines: Four separate variant calling pipelines are implemented for GDC data harmonization. Options: muse, varscan2, somaticsniper, mutect.

References

Ceccarelli, Michele, FlorisP. Barthel, TathianeM. Malta, ThaisS. Sabedot, SofieR. Salama, BradleyA. Murray, Olena Morozova, et al. 2016. “Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma.” Cell 164 (3): 550–63. doi:http://dx.doi.org/10.1016/j.cell.2015.12.028.

Davis, Caleb F, Christopher J Ricketts, Min Wang, Lixing Yang, Andrew D Cherniack, Hui Shen, Christian Buhay, et al. 2014. “The Somatic Genomic Landscape of Chromophobe Renal Cell Carcinoma.” Cancer Cell 26 (3). Elsevier: 319–30.

Huber, Wolfgang, Vincent J Carey, Robert Gentleman, Simon Anders, Marc Carlson, Benilton S Carvalho, Hector Corrada Bravo, et al. 2015. “Orchestrating High-Throughput Genomic Analysis with Bioconductor.” Nature Methods 12 (2). Nature Publishing Group: 115–21.

Network, Cancer Genome Atlas Research, and others. 2012. “Comprehensive Genomic Characterization of Squamous Cell Lung Cancers.” Nature 489 (7417). Nature Publishing Group: 519–25.

———. 2013a. “Comprehensive Molecular Characterization of Clear Cell Renal Cell Carcinoma.” Nature 499 (7456). Nature Publishing Group: 43–49.

———. 2013b. “Integrated Genomic Characterization of Endometrial Carcinoma.” Nature 497 (7447). Nature Research: 67–73.

———. 2014a. “Comprehensive Molecular Profiling of Lung Adenocarcinoma.” Nature 511 (7511). Nature Publishing Group: 543–50.

———. 2014b. “Integrated Genomic Characterization of Papillary Thyroid Carcinoma.” Cell 159 (3). Elsevier: 676–90.

———. 2015. “The Molecular Taxonomy of Primary Prostate Cancer.” Cell 163 (4). Elsevier: 1011–25.

———. 2016. “Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma.” N Engl J Med 2016 (374). Mass Medical Soc: 135–45.

Network, Cancer Genome Atlas, and others. 2012a. “Comprehensive Molecular Characterization of Human Colon and Rectal Cancer.” Nature 487 (7407). Nature Publishing Group: 330–37.

———. 2012b. “Comprehensive Molecular Characterization of Human Colon and Rectal Cancer.” Nature 487 (7407). Nature Publishing Group: 330–37.

———. 2012c. “Comprehensive Molecular Portraits of Human Breast Tumours.” Nature 490 (7418). Nature Publishing Group: 61–70.

———. 2015a. “Comprehensive Genomic Characterization of Head and Neck Squamous Cell Carcinomas.” Nature 517 (7536). Nature Publishing Group: 576–82.

———. 2015b. “Genomic Classification of Cutaneous Melanoma.” Cell 161 (7). Elsevier: 1681–96.